Kaiming He's All-Undergrad Team Achieves Text-to-Image With Only 258M Parameters
量子位 QbitAI39 days agoPaperA new research paper from Kaiming He's lab — notable for having an all-undergraduate team — demonstrates that high-quality text-to-image generation can be achieved with just 258 million parameters. This challenges the prevailing assumption that competitive image synthesis requires multi-billion-parameter models. The work signals a push toward leaner, more accessible generative vision architectures.
Silia: A Tiny Transformer Architecture for Sub-10M Parameter Models
r/LocalLLaMA top day47 days agoPaperA student from India shared their first paper on r/LocalLLaMA, proposing Silia, a Transformer architecture for extremely small models. The idea is to merge attention-style dynamic mixing with SwiGLU-like nonlinear transformation, aiming to save parameters in models under roughly 10M parameters. The author frames the work as an early, small-scale exploration, limited by old hardware and restricted access to larger compute.